<![CDATA[The rapid development of information technology has led to an exponential increase in data volume, requiring efficient and high-performance sorting algorithms. Sorting is one of the fundamental operations in large-scale data processing. This study aims to analyze the performance of the Quick Sort and Merge Sort algorithms in sorting large datasets based on time complexity using Big-O notation. The research method employed is experimental, by implementing both algorithms on datasets of various sizes, then measuring execution time and analyzing their time complexity under best-case, average-case, and worst-case conditions. The results show that Quick Sort performs faster on average with a time complexity of O(n log n), but its performance can degrade to O(n²) in the worst case. Meanwhile, Merge Sort demonstrates more stable performance with a time complexity of O(n log n) in all cases, although it requires additional memory usage. Based on these findings, the selection of sorting algorithms for large-scale data should consider data characteristics and memory requirements to achieve optimal performance.]]>
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